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Interest point detection
8712162 Interest point detection
Patent Drawings:

Inventor: Kirsch
Date Issued: April 29, 2014
Application:
Filed:
Inventors:
Assignee:
Primary Examiner: Desire; Gregory M
Assistant Examiner:
Attorney Or Agent: Kellogg; David C.
U.S. Class: 382/195; 382/264
Field Of Search: ;382/195; ;382/264
International Class: G06K 9/46; G06K 9/66
U.S Patent Documents:
Foreign Patent Documents: 1987928; 101488217; 200920452; 03009579; 2004055724
Other References: Huggett, Anthony, GB Patent Application #1100850.5, filed Jan. 18, 2011. cited by applicant.
Kirsch, Graham, GB Patent Application #1100848.9, filed Jan. 18, 2011. cited by applicant.
H. Bay, "From Wide-baseline Point and Line Correspondences to 3D", Ph.D. Thesis, ETH Zurich, 2006 [online], [retrieved on Jun. 17, 2011]. Retrieved from the Internet: <URL:http://e-collection.library.ethz.ch/eserv/eth:28817/eth-28817-01.- pdf>and <URL:http://e-collection.library.ethz.ch/eserv/eth:28817/eth-28817-02.- pdf>. cited by applicant.
Lowe, David G., "Distinctive Image Features from Scale-Invariant Keypoints", Journal Name: International Journal of Computer Vision, Cover Date: Nov. 1, 2004, Publisher: Springer Netherlands, Issn: 0920-5691, Subject: Computer Science, Start p. 91,End p. 110, vol. 60, Issue: 2, Url: http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94, Doi: 10.1023/B:VISI.0000029664.99615.94, [online], [retrieved on Jun. 17, 2011]. Retrieved from the Internet:<URL:http://people.cs.ubc.ca/.about.lowe/paper/ijcv04.pdf>. cited by applicant.
Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), vol. 110, No. 3, pp. 346-359, 2008, [online], [retrieved on Jun. 17, 2011]. Retrieved from the Internet:<URL:ftp://ftp.vision.ee.ethz.ch/publications/articles/eth.sub.--biwi.- sub.--00517.pdf>. cited by applicant.
Bay, Herbert, Tuytelaars, Tinne, Van Gool, Luc, "SURF: Speeded Up Robust Features", Computer Vision--ECCV 2006, Lecture Notes in Computer Science, Copyright: 2006, Publisher: Springer Berlin / Heidelberg, Start p. 404, End p. 417, vol. 3951,<Url: http://dx.doi.org/10.1007/11744023.sub.--32>. cited by applicant.
S. Winder, Gang Hua, M. Brown, "Picking the best DAISY," cvpr, pp. 178-185, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. cited by applicant.
"Tutorials--SIFT", VLFeat.org, [online], [retrieved on Jun. 17, 2011]. Retrieved from the Internet: <URL:http://www.vlfeat.org/overview/sift.htmlf>. cited by applicant.
Huggett, Anthony, U.S. Appl. No. 13/149,824, filed May 31, 2011. cited by applicant.









Abstract: Interest points are markers anchored to a specific position in a digital image of an object. They are mathematically extracted in such a way that, in another image of the object, they will appear in the same position on the object, even though the object may be presented at a different position in the image, a different orientation, a different distance or under different lighting conditions. Methods are disclosed that are susceptible to implementation in hardware and corresponding hardware circuits are described.
Claim: What is claimed is:

1. A method of identifying candidate interest points in an image having rows and columns of image data, the method comprising: receiving the rows of image data in series; generating downscaled image data by blurring the image data received, wherein the downscaled image data represents the blurred data from a pattern of pixel locations in the received image data; identifying candidate interest points at a first scale byHessian-filtering the image data and detecting extrema in the Hessian-filtered data; discarding the rows of image data in series; identifying candidate interest points at a second scale by Hessian-filtering the downscaled image data and detectingextrema in the Hessian-filtered, downscaled data, wherein at least one of the rows of image data is discarded before all the rows of image data have been received; generating twice downscaled image data by blurring the downscaled image data, wherein thetwice downscaled image data represents the blurred data from a pattern of pixel locations in the once downscaled image data; and detecting candidate interest points at a third scale by Hessian-filtering the twice downscaled image data and detectingextrema in the Hessian-filtered, twice downscaled data.

2. The method of claim 1 comprising discarding at least one of the rows of once downscaled image data before all the rows of once downscaled image data have been generated.

3. The method of claim 1 comprising, for m equals 2 to n, where n is an integer greater than 2: generating m-times downscaled image data by blurring m-1-times downscaled image data, wherein the m-times downscaled image data represents theblurred data from a pattern of pixel locations in the m-1-times downscaled image data; identifying candidate interest points at an (m+1)th scale by Hessian filtering the m-times downscaled image data and detecting extrema in the Hessian-filtered,m-times downscaled data; and discarding the rows of m-1-times downscaled image data in series.

4. The method of claim 3 wherein, for m equals 2 to n-1, at least one of the rows of m-1-times downscaled image data is discarded before all the rows of m-1-times downscaled image data have been generated.

5. The method of claim 4 wherein the received image data is received into and discarded from a line buffer.

6. The method of claim 5 wherein the downscaled image data is also received into a line buffer.

7. The method of claim 6 wherein the received image data is received into and discarded from a line buffer and all the downscaled image data is also received into the same line buffer.

8. The method of claim 7 wherein identifying candidate interest points at a particular scale comprises applying at least three Hessian filters spaced in scale space to the image data and detecting extrema in the resulting Hessian-filtered data.

9. The method of claim 8 wherein identifying candidate interest points at a particular scale comprises applying more than three Hessian filters spaced in scale space to the image data and detecting extrema in the resulting Hessian-filtered dataat more than one scale.

10. The method of claim 9 wherein the Hessian filters are equally spaced in scale space.

11. A circuit for identifying candidate interest points in an image having rows and columns of image data, the circuit comprising: at least one input for receiving the rows of image data in series; a line buffer for storing at most a subset ofthe rows of image data as they are received, whereby at least one of the rows of image data is to be discarded from the line buffer before all the rows of image data have been received; a convolution engine adapted to convolve received image data with ablurring filter and to output blurred data from a pattern of pixel locations in the received image data as downscaled image data, and to convolve received image data with at least three Hessian filters of different scales and to output the Hessianfiltered data; an interest point identifier adapted to receive the Hessian filtered data from the convolution engine, to detect extrema in the Hessian-filtered data to identify candidate interest points and to output the position and scale of thecandidate interest points so identified; and sequencing circuitry adapted to sequence the operation of the circuit to pass received image data from the line buffer to the convolution engine to be convolved with both the blurring filter and the Hessianfilters, to pass downscaled image data from the blurring filter back to the convolution engine to be convolved with the Hessian filters, and to discard the rows of received image data in series, whereby the interest point identifier identifies candidateinterest points at a first scale in the Hessian-filtered received image data and at a second scale in the Hessian-filtered, downscaled data and outputs the position and scale of the candidate interest points so identified.

12. The circuit of claim 11 wherein the sequencing circuitry is adapted to sequence the operation of the circuit to pass downscaled image data from the line buffer to the convolution engine to be convolved with both the blurring filter and theHessian filters, to pass twice downscaled image data from the blurring filter back to the convolution engine to be convolved with the Hessian filters, and to discard the rows of once downscaled image data in series, whereby the interest point identifieralso identifies candidate interest points at a third scale in the Hessian-filtered, twice downscaled data and outputs the position and scale of the candidate interest points so identified.

13. The circuit of claim 11 wherein the sequencing circuitry is adapted to sequence the operation of the circuit, for m equals 2 to n, where n is an integer greater than 2, to pass m-1-times downscaled image data from the line buffer to theconvolution engine to be convolved with both the blurring filter and the Hessian filters, to pass m-times downscaled image data from the blurring filter back to the convolution engine to be convolved with the Hessian filters, and to discard the rows ofm-1-times downscaled image data in series, whereby the interest point identifier also identifies candidate interest points at an (m+1)th scale in the Hessian-filtered, m-times downscaled data and outputs the position and scale of the candidate interestpoints so identified.

14. The circuit of claim 13 wherein the sequencing circuitry is adapted to sequence the operation of the circuit to pass to pass twice downscaled image data, or m-times downscaled image data as the case may be, from the blurring filter back tothe line buffer and then from the line buffer to the convolution engine to be convolved with the Hessian filters.

15. A method of downscaling and organising image data, the method comprising: receiving image data organised into rows and columns; storing the image data in a line buffer organised into rows and columns, wherein the rows of image data arestored in successive rows of the line buffer and, in each row, the image data is stored in successive columns; generating downscaled image data by blurring the image data received, wherein the downscaled image data is organised into rows and columns andrepresents the blurred data from a pattern of pixel locations in the received image data; storing the downscaled image data in the same line buffer, wherein the rows of the downscaled image data are stored in successive rows of the line buffer, at leastone of which also stores received image data and, in each row, the downscaled image data is stored in successive unused columns; generating twice downscaled image data by blurring the downscaled image data, wherein the twice downscaled image data isorganised into rows and columns and represents the blurred data from a pattern of pixel locations in the once downscaled image data; and storing the twice downscaled image data in the same line buffer, wherein the rows of the twice downscaled image dataare stored in successive rows of the line buffer, at least one of which also stores received and once downscaled image data and, in each row, the twice downscaled image data is stored in successive unused columns of the line buffer.

16. The method of claim 15 comprising, for m equals 2 to n, where n is an integer greater than 2: generating m-times downscaled image data by blurring m-1-times downscaled image data, wherein the m-times downscaled image data represents theblurred data from a pattern of pixel locations in the m-1-times downscaled image data; storing the m-times downscaled image data into the same line buffer, wherein the rows of the m-times downscaled image data are stored in successive rows of the linebuffer, at least one of which also stores received and m-1 times downscaled image data and any and all intermediate downscaled image data, and, in each row, the m-times downscaled image data is stored in successive unused columns.

17. The method claim 16 wherein more than one row of the line buffer stores both received image data and downscaled image data.

18. The method of claim 16 wherein more than one row of the line buffer stores received image data, downscaled image data and twice downscaled image data.
Description:
 
 
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